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Sabkha soil is abundant along the Arabian Gulf and Red Sea coasts and is a problematic soil due to its acute water sensitivity and chemical aggressiveness. In many situations, it is required to improve the load carrying capacity of sabkha, and the use of geotextiles was found appropriate. The objectives of this research were to study frictional characteristics of sand-geotextile-sand and sabkha-geotextile-sand interfaces and to compare the pull-out resistance of locally available nonwoven geotextiles taking into account different test parameters. An experimental setup was developed to conduct the pull-out tests. These test results have indicated the existence of three stages of deformation in the geotextile under pull-out testing, which ultimately lead to the slippage of the entire geotextile strip. The use of the pull-out plate reduces the effects of the lateral earth pressure developed on the front wall of the pull-out box and ensures that the free geotextile is kept within the box and, thus, under the required confinement throughout the test. The pull-out tests results indicated that high tensile strength geotextiles require a large pull-out force in the case of the sand-geotextiles and interface, whereas the least extensible geotextile requires the maximum pull-out force in the case of the sabkha-geotextile-sand interface. It was also found that the geotextile surface texture and extensibility are the two main factors, in addition to the mass per unit area of the geotextile, in the case of sabkha-geotextile-sand interface.

Background Artificial intelligence (AI) is gaining traction in medicine and surgery. AI-based applications can offer tools to examine high-volume data to inform predictive analytics that supports complex decision-making processes. Time-sensitive trauma and emergency contexts are often challenging. The study aims to investigate trauma and emergency surgeons’ knowledge and perception of using AI-based tools in clinical decision-making processes. Methods An online survey grounded on literature regarding AI-enabled surgical decision-making aids was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was advertised to 917 WSES members through the society’s website and Twitter profile. Results 650 surgeons from 71 countries in five continents participated in the survey. Results depict the presence of technology enthusiasts and skeptics and surgeons' preference toward more classical decision-making aids like clinical guidelines, traditional training, and the support of their multidisciplinary colleagues. A lack of knowledge about several AI-related aspects emerges and is associated with mistrust. Discussion The trauma and emergency surgical community is divided into those who firmly believe in the potential of AI and those who do not understand or trust AI-enabled surgical decision-making aids. Academic societies and surgical training programs should promote a foundational, working knowledge of clinical AI.

Document clustering as an unsupervised approach extensively used to navigate, filter, summarize and manage large collection of document repositories like the World Wide Web (WWW). Recently, focuses in this domain shifted from traditional vector based document similarity for clustering to suffix tree based document similarity, as it offers more semantic representation of the text present in the document. In this paper, we compare and contrast two recently introduced approaches to document clustering based on suffix tree data model. The first is an Efficient Phrase based document clustering, which extracts phrases from documents to form compact document representation and uses a similarity measure based on common suffix tree to cluster the documents. The second approach is a frequent word/word meaning sequence based document clustering, it similarly extracts the common word sequence from the document and uses the common sequence/ common word meaning sequence to perform the compact representation, and finally, it uses document clustering approach to cluster the compact documents. These algorithms are using agglomerative hierarchical document clustering to perform the actual clustering step, the difference in these approaches are mainly based on extraction of phrases, model representation as a compact document, and the similarity measures used for clustering. This paper investigates the computational aspect of the two algorithms, and the quality of results they produced.

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Half-scaled reinforced concrete frame of two storeys and two bays with unreinforced masonry (URM) infill walls was subjected to base excitation on a shake table for seismic performance evaluation. Considering the high seismic hazard Zone IV of Pakistan, reinforcement detailing in the RC frame is provided according to special moment resisting frames (SMFRs) requirement of Building Code of Pakistan Seismic-Provisions (BCP SP-2007). The reinforced concrete frame was infilled with in-plane solid masonry walls in its interior frame, in-plane masonry walls with door and window openings in the exterior frame, out-of-plane solid masonry wall, and masonry wall with door and window openings in its interior frame. For seismic capacity qualification test, the structure was subjected to three runs of unidirectional base excitation with increasing intensity. For system identification, ambient-free vibration tests were performed at different stages of experiment. Seismic performance of brick masonry infill walls in reinforced concrete frame structures was evaluated. During the shake table test, performance of URM infill walls was satisfactory until design ground acceleration was 0.40g with a global drift of 0.23%. The test was continued till 1.24g of base acceleration. This paper presents key findings from the shake table tests, including the qualitative damage observations and quantitative force-displacement, and hysteretic response of the test specimen at different levels of excitation. Experimental results of this test will serve as a benchmark for validation of numerical and analytical models.

Background Shared decision-making (SDM) between clinicians and patients is one of the pillars of the modern patient-centric philosophy of care. This study aims to explore SDM in the discipline of trauma and emergency surgery, investigating its interpretation as well as the barriers and facilitators for its implementation among surgeons. Methods Grounding on the literature on the topics of the understanding, barriers, and facilitators of SDM in trauma and emergency surgery, a survey was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was sent to all 917 WSES members, advertised through the society’s website, and shared on the society’s Twitter profile. Results A total of 650 trauma and emergency surgeons from 71 countries in five continents participated in the initiative. Less than half of the surgeons understood SDM, and 30% still saw the value in exclusively engaging multidisciplinary provider teams without involving the patient. Several barriers to effectively partnering with the patient in the decision-making process were identified, such as the lack of time and the need to concentrate on making medical teams work smoothly. Discussion Our investigation underlines how only a minority of trauma and emergency surgeons understand SDM, and perhaps, the value of SDM is not fully accepted in trauma and emergency situations. The inclusion of SDM practices in clinical guidelines may represent the most feasible and advocated solutions.